Inside a plan experience study LAPERS - September 2019 Inside a plan experience study Trustee Education session Gary and Greg Curran Consulting Actuaries G. S. Curran & Company, Ltd. 10555 N. Glenstone Place Baton Rouge, Louisiana 70810 (225)769-4825

Why do plans need an Experience Study? Periodic studies are a necessary part of the process used in determining the actuarially required contributions for a retirement system. Proper valuation requires reasonable assumptions It is improbable that future experience will exactly mirror past experience, but we must begin by studying past experience.

Why do plans need an Experience Study? Ultimately, plan costs depend on actual experience Liabilities and contribution levels are calculated based on plan assumptions Some baseline is necessary to set assumptions Sometimes the best estimate of the future is the past

What is the goal of a formal Plan Experience Study? To review all plan assumptions contained within the actuarial valuation model versus recent plan experience in order to set appropriate future assumptions. To consider factors other than recent experience relevant in setting reasonable future plan assumptions.

What’s included in a typical Plan Experience Study? Analysis of various plan factors over a specified period (typically 3 to 5 years) Review of: Decrement Rates – Mortality, Retirement, DROP Entry (if applicable), Disability, and Withdrawal Average DROP Participation Period, Percent Retiring at the End of DROP, Post-DROP Retirement Rates Back-DROP Utilization Rates Economic Factors – Inflation, Salary Increase Rates, Valuation Interest Rates Other Factors – Family statistics, average leave converted at retirement, vesting election percentage, Percent Married

What’s Needed? Data from prior valuations (additional review can be necessary to be sure all decrements are identified) Standard tables such as: Mortality Tables, Mortality Improvement Scales, Disability Incidence Tables Inflation forecast data, projected long-term rates of return, expected standard deviations of returns, correlations of returns by asset class. Government statistics

Important Study Definitions Decrement – A decrement is an exit from the plan during the study period. Typical decrements – Retirement, death, disability, and withdrawal. Our valuation program uses a fifth decrement for many systems – DROP entry

Important Study Definitions (continued) Exposures – The sum of members over the experience study period who are eligible for a certain decrement counted for each year they are eligible. Example: 200 members eligible to retire in 2017 and 180 members eligible to retire in 2018 = 380 exposures over the period 2017 and 2018. Note: some of these may be the same people.

Measuring Decrement Rates Divide the number of decrements by the appropriate number of exposures to determine decrement rate Rates can be set based on age Ex: The number of retirements at age 60 during the study period The number of retirement exposures at age 60 Rates can be set based on service Ex: The number of withdrawals between 1 and 2 years of service The number of withdrawal exposures between 1 and 2 years Rates can be set based on age and service combinations

Developing New Decrement Rates Decrement studies develop a set of actual rates at each age or at each duration based on data during the study period. This data is compared to the expected rates at each age or duration based on the plan’s assumptions prior to the study. Actual observed rates may be smoothed and/or adjusted for expected differences in future experience.

Why make adjustments? It is often necessary to adjust raw past experience to account for factors which will have an impact on the future, such as expected changes in economic conditions or new benefit structures. Past experience is often of limited value where the size of the group or frequency of events is relatively small.

Retirement Rate Example Age Actual Retirements Exposures Raw Rate Current Valuation Rate Current Projection Smoothed Rate Rounded Smoothed Rate Smoothed Projection 55 4 62 0.065 0.06 3.72 0.071 0.07 4.34 56 5 70 4.20 0.067 4.9 57 3 60 0.05 3.60 0.064 3.6 58 2 54 0.037 3.24 0.063 59 53 0.075 3.18 3.71 23 344 0.12 41.28 0.069 24.08 61 26 272 0.096 32.64 0.076 0.08 21.76 14 195 0.072 23.40 0.086 0.09 17.55 63 18 165 0.109 19.80 0.100 0.10 16.5 64 12 128 0.094 15.36 0.117 65 10 98 0.102 0.18 17.64 0.137 0.14 13.72

Graphical Review

Should we use data from the study period to set future rates? Is there reason to believe that the future will differ from the study period? Example: An exceptionally large pay raise in a single employer plan might have affected retirement patterns as members wait for enough years at the new salary to set final average compensation. Were there ages with no decrements? Can we assume because no one retired at age 84 during the study period that no one will ever retire at age 84? Do we have sufficient data during the study period to set rates? What about a recently created new hire tier?

Issues with New Tiers of Benefits Often, new benefit tiers lead to minimal historical data to use in setting future assumptions. For example: What can be done to set retirement rates for a benefit tier created for new hires who joined the system on or after 1/1/2013 in a plan with a single rule: 10 years at age 60? Perhaps, no one has yet met the eligibility standard. May have to look to other data within the system to set future assumed rates of decrement.

Should we adjust recent rates? Should we simply use the rates implied by the raw data? What about ages/durations with no incidence of decrement? What about data with rates that are highly volatile by age/duration? Perhaps we should make ad hoc changes for anomalies that are not expected to repeat, like filling in zero rates. Or, should we use a smoothing methodology?

Graduation Mathematical technique to remove the “noise” from the result and discover the underlying trend: Form of smoothing or curve fitting. Find an equation that best fits the reported data. Example: Whittaker- Henderson

Example – Retirement Decrement

Setting Valuation Interest Rate The valuation interest rate plays a significant role in calculating the liabilities of a defined benefit retirement system Every projected benefit payment is discounted from the projected payment date to the valuation date using the valuation interest rate Retirement systems have differing sensitivity to changes in valuation interest rate based on benefit structure, assumptions, and the maturity of the system.

Setting Valuation Interest Rate Inputs used in studying the valuation interest rate: Projected Rates of Return by Asset Class Projected Standard Deviation of Returns by Asset Class Correlation Coefficients among various asset classes System’s specific asset allocation or target asset allocation

Setting Valuation Interest Rate Inputs are collected from multiple sources, such as: Asset class return/standard deviation projections collected from the plan’s investment consultant Asset class return/standard deviation projections from other consultants and investment professionals Information from surveys like the Horizon Actuarial Survey Inflation projections from the Federal Reserve, Business Surveys, and other government and private agencies.

Valuation Interest Rate The inputs are used to develop: Long-term inflation reasonable range Expected Rate of Return reasonable range for the system’s portfolio Expected standard deviation of the portfolio return Two separate expected return and standard deviation projections are developed. 1st - Based solely on the system’s investment consultant’s projections 2nd - Based upon the consultant average projections

Setting Valuation Interest Rate Process: Using the system’s Net Portfolio Mean Nominal Return and expected standard deviation, we can generate a set of stochastic trials to model portfolio returns based upon the presumption that portfolio returns will unfold according to the normal distribution

Setting Valuation Interest Rate Outputs: Mean geometric return over a future period Probabilities of achieving various stipulated rates of return are determined by running 10,000 stochastic trials Using the percentiles determined through stochastic simulation, we can set a reasonable range for the valuation interest rate. The Board of Trustees can select the valuation interest rate from within this range based on their own risk tolerance and other considerations.

Wrap Up This presentation only touched on the many assumptions reviewed in an experience study. Assumptions are often based on plan specific data, but in the end they are simply reasonable representations of what is expected to occur. Liability measurements depend heavily on assumptions. Actual plan liabilities and payments depend not on assumptions but on what actually happens.